Right now i have five active threads running.
Mechanistic interpretability research (just started, genuinely curious). An RL experiment with Prime Lab (toy project, following breadcrumbs). Daily content (non-negotiable, shipping). Amazon plus some client projects outside of business hours (performing, also non-negotiable). And an ALOHA-mini robotics kit arriving in 2-3 weeks that i haven't even assembled yet.
The thing i struggle with is how people seem to know so easily what their area of focus is. Everyone in ML group chats seems to have it figured out. "I'm a diffusion person." "I do RL." "I focus on interpretability." Meanwhile i'm over here sampling everything that looks interesting at the frontier.
The answer i'm landing on: you don't know in advance. You try things and notice what pulls you back.
The Five Threads (Honestly)¶
Let me lay out the current stack:
Daily content is non-negotiable. i'm shipping regardless. This is the forcing function that keeps me writing, and writing keeps me thinking.
Amazon plus client work is the day job (and some evenings). Also non-negotiable. i'm performing here, and the work is interesting enough that i don't feel like i'm grinding through something i hate.
Mechanistic interpretability is the new thing. Just started. Reading papers, running experiments, trying to understand how these models actually work underneath.
RL experiments with Prime Lab came from a connection i made when Polymarket data dropped the same week Prime Lab announced their thing. Fun, but more breadcrumb-following than deep commitment.
ALOHA-mini robotics is still in a box (well, it will be when it arrives). i want to step away from the computer and do physical hardware work. Different kind of thinking.
Here's the tension: at some point exploration has to collapse into exploitation or you never get deep enough to be differentiated. You can't be a generalist forever and expect to contribute something meaningful to any single field.
But also: i'm 27. This isn't pulling me from my day job. This isn't causing me to slack. At the end of the day this is exploration to surface areas of what i might be interested in. The stakes aren't that high.
Forcing Functions: External vs Internal¶
External forcing functions (work, deadlines, hardware arriving) are nice because they put you in a situation where you have no choice but to make progress. And making progress brings about biases or assumptions you may have made about the field.
Sometimes it invalidates things you assumed from the outside based on romanticized ideas of what people do in certain areas.
At Wendy's, data scientists were my customers. i was the bridge between research and implementation. That taught me something about what ML work actually looks like in production that i couldn't have learned from papers alone.
Internal forcing functions (side projects, curiosity-driven work) are different. When you're working on a lot of projects on the side, a lot of it is curiosity-driven. There's no external pressure.
i do have ADHD tendencies. i'm very shiny object oriented. i like innovation, i like doing new things, i like trying new things. That's just true.
The question: without external forcing functions, what makes you commit to one thing over another?
The Signal: Involuntary Attention¶
Here's the filter that actually works.
Of these five threads, when i'm not actively working on any of them, my mind keeps going back to the mechanistic interpretability stuff. i'm thinking of new approaches. Thinking of new aspects of how i can modify my current experiment to maybe bring about new findings or just be a little more creative in my approach.
The RL/Prime Lab experiment was different. It was fun, but it wasn't pulling at me. It was following breadcrumbs (the timing of the Polymarket data drop + Prime Lab announcement = connection made). That's fine, but that's not the same thing.
The contrast that revealed the pattern for me was Polychromic LoRA.
Previously i was doing Polychromic LoRA experimentation. Style transfer stuff, fine-tuning approaches. And i quickly found that it was kind of boring. i was optimizing for the reply guy trend on Twitter. i was just trying to do the hype thing, the cool thing, rather than something that felt naturally interesting.
Hype-driven feels different from genuine interest. You can tell. The hype projects feel like you're trying to position yourself. The genuine interest projects feel like you can't stop thinking about them even when you should be doing something else.
What "Novel" Actually Means¶
Success for me would look like feeling content and satisfied with the piece of work, whether it be:
- uncovering something novel
- pushing the field forward
- even just learning something new on my own
At the minimum, putting up a blog post about the stuff i've done is sufficient. That's the lowest bar, and it's still worth something.
There are three different levels here:
Novel to you means you learned something new. You understand something you didn't understand before. That has value even if nobody else sees it.
Novel to your audience means it's publishable content. Other people can learn from what you did. That's a contribution.
Novel to the field means actual research contribution. This is where you're pushing boundaries, finding things nobody has found before.
Obviously my goal is to push the field forward. i want to do something meaningful. i think that's kind of where purpose lies sometimes.
But i don't want to put my self-worth in that too much.
If the only outcome that counts is "novel to the field," you're setting yourself up for misery. Most experiments don't produce novel results. Most research threads don't pan out. If you need the highest bar every time, you'll either stop trying or hate yourself for failing.
The spectrum matters. All three levels count.
The Real Interest Underneath¶
Here's what i'm starting to notice about my own patterns.
Maybe just training models is not my interest. Maybe the interest is where neuroscience and psychology bridge the gap with computer science.
At the end of the day i'm a neuroscience major. i switched into cognitive science because i wanted to get into computer science and have actual technical courses. But the underlying curiosity never went away. i'm very curious about psychology, about the way the mind works.
Mechanistic interpretability sits at that intersection. You're trying to understand how these models think. What computations are happening. How do concepts get represented. It's not just about making the model perform better (though that matters too). It's about understanding the thing.
That's why it pulls.
When i trace the thread back, it goes: neuroscience → cognitive science → computer science → ML → mechanistic interpretability. It's the same underlying interest in "how does thinking work" expressed through different tools at different stages of my life.
i didn't plan that. i'm noticing it in retrospect.
Surface Area for Luck¶
Even me writing these posts is about increasing surface area for luck.
Luck at the end of the day can be controlled by volume. The more work i put out, the more content i put out, i give opportunity for other people to interact with my stuff. Then i can have cool conversations or talk to cool people or maybe get involved in a cool opportunity.
All this is just increasing your surface area for opportunities. That's kind of just what luck is.
Exploration isn't aimless. It's building optionality.
Every thread i'm exploring is another potential path. Most won't pan out. Some will lead somewhere interesting. A few might connect to opportunities i can't predict right now.
The person who explored mech interp for six months before the field exploded had optionality the person who waited didn't have. The person who built robotics skills before humanoid robots became the hot thing had optionality the person who chased the current trend didn't have.
You can't predict which explorations will pay off. You can control how many you try.
What I'm Actually Learning¶
This isn't prescriptive advice. i don't know what you should focus on. i barely know what i should focus on.
But here's what i'm noticing for myself:
Involuntary attention is the signal. What pulls your mind when you're not working on it? That's probably worth exploring more.
Hype-driven feels different from genuine interest. The Polychromic LoRA test: does this feel like chasing the cool thing, or genuine curiosity? i could tell the difference once i paid attention.
The spectrum of success matters. "Learned something" to "pushed the field" are all valid outcomes. Don't need the highest bar every time to justify the exploration.
Forcing functions will emerge. External deadlines, hardware arriving, opportunities appearing. The world will eventually force you to commit to something. In the meantime, the sampling is fine.
i think at the end of the day what i'm realizing is it's just by trying a bunch of things, doing a bunch of things, seeing what actually matters to you.
The answer isn't to stop sampling. It's to pay attention to what keeps pulling you back.